



import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_		 =					logging.get_logger(__name__)

UpperCAmelCase_		 =					{
    """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
    """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/config.json""",
}


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: Any =  "xlnet"
 a__					: Dict =  ["mems"]
 a__					: List[str] =  {
     "n_token": "vocab_size",  # Backward compatibility
     "hidden_size": "d_model",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }

 def __init__(    self					:					int			,						__lowerCAmelCase					:					Dict=3_20_00			,						__lowerCAmelCase					:					List[str]=10_24			,						__lowerCAmelCase					:					Dict=24			,						__lowerCAmelCase					:					Optional[Any]=16			,						__lowerCAmelCase					:					Dict=40_96			,						__lowerCAmelCase					:					Any="gelu"			,						__lowerCAmelCase					:					int=True			,						__lowerCAmelCase					:					List[str]="bi"			,						__lowerCAmelCase					:					Dict=0.02			,						__lowerCAmelCase					:					Union[str, Any]=1E-12			,						__lowerCAmelCase					:					Optional[Any]=0.1			,						__lowerCAmelCase					:					Optional[Any]=5_12			,						__lowerCAmelCase					:					List[str]=None			,						__lowerCAmelCase					:					Tuple=True			,						__lowerCAmelCase					:					Tuple=False			,						__lowerCAmelCase					:					Optional[Any]=False			,						__lowerCAmelCase					:					Union[str, Any]=-1			,						__lowerCAmelCase					:					Optional[Any]=False			,						__lowerCAmelCase					:					Any="last"			,						__lowerCAmelCase					:					List[Any]=True			,						__lowerCAmelCase					:					Tuple="tanh"			,						__lowerCAmelCase					:					int=0.1			,						__lowerCAmelCase					:					str=5			,						__lowerCAmelCase					:					str=5			,						__lowerCAmelCase					:					List[str]=5			,						__lowerCAmelCase					:					List[str]=1			,						__lowerCAmelCase					:					Optional[int]=2			,						**__lowerCAmelCase					:					List[str]			,						)   ->	Tuple:
    _A      =       vocab_size
    _A      =       d_model
    _A      =       n_layer
    _A      =       n_head
    if d_model % n_head != 0:
       raise ValueError(f'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0'''      )
    if "d_head" in kwargs:
       if kwargs["d_head"] != d_model // n_head:
          raise ValueError(
              f'''`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})'''      )
    _A      =       d_model // n_head
    _A      =       ff_activation
    _A      =       d_inner
    _A      =       untie_r
    _A      =       attn_type

    _A      =       initializer_range
    _A      =       layer_norm_eps

    _A      =       dropout
    _A      =       mem_len
    _A      =       reuse_len
    _A      =       bi_data
    _A      =       clamp_len
    _A      =       same_length

    _A      =       summary_type
    _A      =       summary_use_proj
    _A      =       summary_activation
    _A      =       summary_last_dropout
    _A      =       start_n_top
    _A      =       end_n_top

    _A      =       bos_token_id
    _A      =       pad_token_id
    _A      =       eos_token_id

    if "use_cache" in kwargs:
       warnings.warn(
           '''The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'''
           ''' instead.'''			,						__lowerCAmelCase			,						)
       _A      =       kwargs['''use_cache''']

    _A      =       use_mems_eval
    _A      =       use_mems_train
    super().__init__(pad_token_id=__lowerCAmelCase			,						bos_token_id=__lowerCAmelCase			,						eos_token_id=__lowerCAmelCase			,						**__lowerCAmelCase      )

 @property
 def     snake_case_      (    self					:					Optional[Any]      )   ->	Union[str, Any]:
    logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''      )
    return -1

 @max_position_embeddings.setter
 def     snake_case_      (    self					:					Tuple			,						__lowerCAmelCase					:					Optional[Any]      )   ->	Dict:
    # Message copied from Transformer-XL documentation
    raise NotImplementedError(
        f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''      )








'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:   Tuple					=		logging.get_logger(__name__)

lowerCAmelCase:   Any					=		{
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """pegasus"""
    lowercase__					=       ["""past_key_values"""]
    lowercase__					=       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







    def __init__( self					:    Union[str, Any]		,     __snake_case					:    Tuple=5_02_65		,     __snake_case					:    List[Any]=10_24		,     __snake_case					:    Optional[Any]=12		,     __snake_case					:    Tuple=40_96		,     __snake_case					:    str=16		,     __snake_case					:    int=12		,     __snake_case					:    Tuple=40_96		,     __snake_case					:    int=16		,     __snake_case					:    Tuple=0.0		,     __snake_case					:    Any=0.0		,     __snake_case					:    Any=True		,     __snake_case					:    Optional[int]=True		,     __snake_case					:    Any="gelu"		,     __snake_case					:    str=10_24		,     __snake_case					:    Dict=0.1		,     __snake_case					:    List[Any]=0.0		,     __snake_case					:    str=0.0		,     __snake_case					:    str=0.02		,     __snake_case					:    Union[str, Any]=0		,     __snake_case					:    List[Any]=False		,     __snake_case					:    Optional[Any]=0		,     __snake_case					:    str=1		,     __snake_case					:    Any=1		,     **__snake_case					:    int		,     ):
      a		:   Optional[int]	  =	vocab_size
      a		:   List[str]	  =	max_position_embeddings
      a		:   List[Any]	  =	d_model
      a		:   Tuple	  =	encoder_ffn_dim
      a		:   List[str]	  =	encoder_layers
      a		:   str	  =	encoder_attention_heads
      a		:   str	  =	decoder_ffn_dim
      a		:   Optional[int]	  =	decoder_layers
      a		:   Optional[int]	  =	decoder_attention_heads
      a		:   Optional[int]	  =	dropout
      a		:   Any	  =	attention_dropout
      a		:   str	  =	activation_dropout
      a		:   List[Any]	  =	activation_function
      a		:   Union[str, Any]	  =	init_std
      a		:   List[Any]	  =	encoder_layerdrop
      a		:   Union[str, Any]	  =	decoder_layerdrop
      a		:   List[Any]	  =	use_cache
      a		:   Any	  =	encoder_layers
      a		:   Optional[int]	  =	scale_embedding  # scale factor will be sqrt(d_model) if True
      super().__init__(
          pad_token_id=__snake_case		,     eos_token_id=__snake_case		,     is_encoder_decoder=__snake_case		,     decoder_start_token_id=__snake_case		,     forced_eos_token_id=__snake_case		,     **__snake_case		,     )







    @property
    def      lowercase_			( self					:    int      ):
      return self.encoder_attention_heads







    @property
    def      lowercase_			( self					:    int      ):
      return self.d_model